Glean just crossed $300M in top-line revenue seven years in, and its founder gave up his real playbook in a five-minute hallway interview over lunch.

Arvind Jain, the 2x unicorn founder behind Rubrik and Glean and a decade-long Google search engineer, broke down who to hire first, when to raise, what investors actually bet on, and how Glean quiewetly became the layer enterprises use to cut AI costs by close to 6x. We went through the entire conversation question by question so you can steal the moves without watching a single minute.

In this guide you’ll find:

1. The origin story: ‘Google for your work life’ (and 20 engineers before anyone else)

2. Arvind’s fundraising rules: raise fast, dilute early, and know investors bet on you not the idea

3. We asked about Uber burning its AI budget: is enterprise AI ROI actually real?

4. The use cases nobody predicted, and who actually buys Glean

5. Openrouter getting acquired by Stripe 💰

6. Will AI erase all jobs as Dario Amodei says?

7. The OpenAI panel: a 3-layer playbook for deploying agents safely

8. The Enterprise AI Playbook by Glean

1. The origin story: ‘Google for your work life’ (and 20 engineers before anyone else)

We opened with the classics: how did you end up here, who do you hire first after raising your first round, and how do you communicate the vision to so many engineers?

The wedge was a problem Arvind lived, not one he researched. At Rubrik, the enterprise data security company he co-founded, fast growth quietly wrecked internal productivity. “Everybody would complain about not being able to find things inside the company or connect with the right people to get help,” Arvind told us. He had spent over a decade building search systems at Google, so the fix was obvious to him: a real search product for all of a company’s data and knowledge. That became Glean in early 2019. Before that, he grew up in India and moved to the States in the ‘90s for grad school, and he’s been in the tech industry — and based in San Francisco — ever since.

His answer to “who do you hire first” was one word: builders. “The first 10 people that we hired at Glean, they were all engineers. Maybe even the first 20, I don’t remember hiring anybody outside of R&D,” Arvind said. Office, devices, food, early customers, he handled all of that himself so the product team could stay heads-down until the product hit maturity.

He could hire 20 engineers around one idea because the idea fit in a sentence. “It’s a Google for you in your work life. It’s a place where you go and ask questions when you’re looking for information or answers and we’ll bring the right information back to you,” Arvind said. A pitch this simple removes the friction of explaining what you’re building. But he flags the harder half: people have to believe it will be big. “They’ve got to feel that it’s going to be something big.”

The steal: if you can’t recruit engineers around your idea in one sentence, the idea is too complicated to fund and too complicated to build. Pressure-test your mission on a non-technical friend this week. If they can repeat it back, you can hire around it.

2. Arvind’s fundraising rules: raise fast, dilute early, and know investors bet on you not the idea

Next we asked when a founder should raise at all — bootstrapping versus raising money — and whether raising was easier the first time at Rubrik or the second time with Glean.

Arvind’s capital strategy is deliberately aggressive, and it’s built to protect focus, not valuation. “I like to go big and go and go big fast. So if you can, then I would recommend that, just raise capital. What’s the downside?” he told us. His reasoning is operational: he does not want to spend every month calculating whether the company can afford one more engineer. Raising more removes that recurring distraction.

He directly rejects the most common counterargument. “Sometimes people feel that, oh, is it too early to dilute the company? And I don’t really believe in that,” Arvind said. His view is that the drag of under-capitalization costs more than the dilution.

The deeper insight came from our second question: raising was definitely easier the second time, and the reason is about what investors are actually buying. “Investors are actually never looking to invest in great ideas. They’re actually, they’re looking to invest in people,” Arvind said. His logic: any early idea gets transformed, refined, probably changed into something completely different once you talk to customers, so investors are underwriting your persistence, not your pitch deck. A track record signals you’ll “stay on the mission” and not give up.

He also reads the current market as unusually generous. “There’s a lot of capital that is actually waiting to be deployed” on AI ideas, Arvind said, so founders “should feel really, really, really comfortable” they’ll be able to raise. Note this is his read, not a hard figure.

The steal: frame your raise around the person and the persistence, not the perfect idea, and if you can raise enough to stop thinking about hiring math for 18 months, take it. Run the raise as a distraction-elimination decision, not a valuation-optimization one.

3. We asked about Uber burning its AI budget: is enterprise AI ROI actually real?

We put the reported case of Uber burning its 2026 AI budget in four months without seeing ROI directly to Arvind. He didn’t dodge it. “It’s a wide spectrum, but businesses are definitely getting some value from AI today. If they’re getting none, they would have stopped by now,” he said.

The clearest value, per Arvind, is basic knowledge-seeking that makes every employee “more productive, more knowledgeable,” and “that’s not in contention.” The catch: “it’s hard to measure. The fact that, you know, people can work faster and, you know, oftentimes, you know, that doesn’t translate into like your P&L statement.” The companies seeing real results are larger ones with intentional programs, good metrics, and systematic process-by-process adoption — “it is hard, it takes a lot of time, and AI vendors kind of made it seem like you’ll automatically and instantly get all these value savings.” Reality, he said, is “like any other technology where you have to provide a lot of love and care and investment.”

That’s why Glean sells a long-term engagement, not a switch you flip. “We bring our platform so you immediately get those productivity savings, you get a great coworker, but then the real business value, like, you know, we spend many, many months understanding, you know, their business and helping them build those agents and get them to production level of quality,” Arvind said.

How Glean is using this to win. Arvind’s Glean:GO keynote turned the ROI gap into Glean’s entire product pitch. He named the failure mode most enterprises are living: employees now work for the AI instead of the reverse. “Today employees spend a lot of time coaching AI agents and are feeling overburdened with that. This is bot sitting. We are spending too much time working for AI as opposed to AI working for us,” he said. Botsitting — employees feeding context, supervising, and cleaning up after AI agents — has become a significant drain on worker time: Glean’s Work AI Institute, surveying 6,000 full-time digital workers, found employees spend 6.4 hours a week on it, more time than they spend using AI to actually produce work. In the keynote he laid out the three pains every enterprise names: struggling to bring the right context to agents, AI costs running higher than they can afford, and the proliferation of AI-written documents (SLOP), skills, and agents that are hard to manage and secure. Every one of those pains maps to a Glean feature.

On the OpenAI panel he was blunt about the near-term math: “AI is often becoming more expensive than the business value that it is delivering in the short order... We need to make sure companies don’t run out of money before that happens.” Positioning Glean as the platform that closes that gap — cheaper, better-contexted agents — is the commercial answer to the ROI question we asked.

The steal: stop selling (or buying) instant ROI. Set the expectation that production-quality agents take months of context-building, and instrument the specific business processes where you can actually measure cost-per-task before and after. Pick one high-cost, high-human-involvement process and commit to a multi-month build rather than spraying pilots.

4. The use cases nobody predicted, and who actually buys Glean

We asked four go-to-market questions in a row: have customers used Glean in ways you never imagined, who do you reach out to in the organization, do customers build the agents themselves or do you send in your engineers, and where are your customers?

Because Glean is horizontal, the entry point is the person who owns company-wide AI. “Typically we’ll start with the CIO. You know, the CIO typically thinks about how to bring company-wide AI technologies,” Arvind told us. From there, Glean expands to departmental leaders when the value is concentrated. “Sometimes, you know, we do work with departmental leaders too, because for example, for salespeople, for CROs, we do a lot.”

The footprint is mid-to-large enterprises, weighted to the US. “About 70% of the business is probably US and then 30% is the rest of the world right now,” Arvind said, naming Europe, Asia, Canada, and Latin America as the rest. (Those percentages are Arvind’s stated figures.) Named customers on stage at Glean:GO included General Motors (helping engineers design cars faster), McCarthy (construction management), and Navita (patient support), showing how far the horizontal platform stretches across industries.

The use cases prove the “chief of staff” positioning, and Arvind admits almost none were planned. “Almost all use cases are things that we didn’t imagine people would actually use our product for,” he said. The surprises: a performance-management agent that “writes the first drafts” of annual reviews for individuals and managers, customers “responding to RFPs” with AI, teams “reviewing legal contracts with AI,” and salespeople using AI “to figure out what messages to send to which one of their accounts.” His mental model: “since Glean is so horizontal and it connects to all enterprise systems, it sort of functions as your chief of staff.”

Who builds the agents once Glean is in? The customers do, with Glean enabling them. When we asked whether Glean sends in its own engineers, Arvind said customers build the agents themselves, backed by Glean’s “training and enablement programs,” and “sometimes we actually bring our partners to help build agents for our customers.”

The steal: for a horizontal platform, sell the platform to the CIO and the sharpest single-department win (CRO/sales) in parallel, then let unpredicted use cases pull expansion. Ship analytics that surface which unplanned workflows customers invent, because those are your next feature roadmap and your next case study.

5. Is Glean just a model router? The context moat and the 81% cost cut

Our spiciest question: OpenRouter — reportedly being acquired by Stripe — what do you think of that? It got the most strategically important answer of the whole interview.

Arvind said he’s very familiar with them: “They were one of the first model routing companies, but more sort of model access company than routing.” Glean does some of the same things OpenRouter did, but mostly in the enterprise context for large enterprises, and the business is very different: model access “is actually only one part of our overall solution.” That’s why Arvind resists being called a model router even though Glean does model access. “Largely, like, you know, we think of Glean as the context and intelligence company,” he told us, describing the goal as “bringing the collective human intelligence and the know-how, the knowledge and experience of your enterprise in one place and then make it available to AI systems.”

How Glean is using this to win. The “context company” framing isn’t marketing — it’s the entire thesis, and it rests on one number: structured data is a rounding error of what a company actually knows. “Structured data and data lakes capture only 5 to 10% of all enterprise knowledge,” Arvind said in his Glean:GO keynote, and documents like playbooks and SOPs only go so far. “Much of the real know-how of how work happens lives in people’s heads.” (The 5-10% figure comes from his talk; treat it as his framing.) The good news, per Arvind, is that the other 90-95% “does surface in emails, messages, meeting notes, exception approvals” as people do their regular work.

Capturing that is the moat, and Arvind draws a hard line between Glean’s approach and the connector-first competition. Glean’s context engine “combines advanced retrieval with a powerful enterprise graph” that maps the people, customers, teams, projects, and the relationships between them. “That deeper understanding of your business, how it operates, is what Glean sets itself apart from the industry’s typical approach of delivering shallow context via MCP,” Arvind said, adding: “Glean is the only context and intelligence company that truly understands your enterprise.” Glean CPO Emrecan Dogan sharpened the same point in the company’s Glean:GO press release: “Models are getting better and more interchangeable. Understanding the enterprise is not. We designed Glean around that reality in 2019.”

And context is exactly how Glean wins the cost war. The headline number from Glean:GO: “Glean saves 81% on token costs compared to Claude, with Glean response preferred 78% of the time,” Arvind said. (Percentages are Arvind’s own from the keynote.) Independent observers noticed the token gap too; Rohan Paul flagged the 1.3M vs 4.4M split on X.

As we put it in the interview, “the cost is even like 6 times or close to 6 times less than other options like Claude Cowork.”

The waste Glean attacks is architectural, not technical. “Most systems still point the most expensive models at the simplest of the tasks. That’s not really a limitation of AI, it’s an architecture choice,” Arvind said. Glean’s Pareto frontier analysis makes the spread concrete: across 1,000 enterprise tasks and 37 model/reasoning configurations, the cost spread between the cheapest and most capable options is dramatic for modest quality differences. There is no single best model, so routing is the point. Auto-routing uses a small specialist model to pick the right reasoning effort per task.

The second half of the savings comes back to context. “Quality context stops AI from having to reconstruct it at runtime, which burns tokens and hands back answers it can’t use,” Arvind said. Better context means fewer tokens and better answers at the same time. The delivery layer is the AI Gateway, which pairs model choice with governance: closed and open models routed through one place with Glean Protect controls, so customers can pick models that meet their data retention and regulatory requirements.

The steal: the durable asset in enterprise AI is the years-long graph of how work actually happens, not a thinner MCP connector to the same systems. Then audit which model is handling your simplest, highest-volume tasks — if a frontier model is answering questions a mid-tier model could answer, you’re paying the 36x spread for a 22% gain you don’t need. Build (or buy) a routing layer that matches task difficulty to model cost, and measure it in dollars per task, not tokens.

6. Will model companies eat every startup idea (and what happens to jobs)?

We closed the interview with the two questions on every founder’s mind: Dario Amodei’s much-quoted prediction that AI will erase 50% of white-collar jobs in the next 5 years, and what Arvind would say to founders starting a company right now.

On jobs, he’s a clear skeptic of the doomsday number. “It’s a very bold statement and obviously I don’t think there’s any kind of deep analysis or research that was done to come up with the number 50. The fact that the number is 50 means it’s more figurative... But I kind of disagree with that regardless. I don’t think people are going to be replaced with AI in the sense that there are no jobs for us anymore. I think we’ll all have jobs and I think the nature of our jobs is going to change” — the same way his own work has continuously evolved in tech.

On founders, Arvind spends real time reassuring people who fear the model companies will eat everything. He hears it constantly: “Every idea they come up with, you know, it feels like, you know, you can just use an AI model or the model companies, you know, just come and do that.” His worry is that founders “start to give up too soon” and never build.

His counter is that change is the entrepreneur’s raw material. “AI changes, you know, if anything, you know, that AI changes, it actually creates more opportunity for entrepreneurs. Because it brings change, and whenever there is change, that is great for entrepreneurs,” Arvind said. And he doesn’t think the model layer can cover the surface area: “Model companies won’t be able to meet even 10% of all the needs, you know, that the market is going to have.”

The OpenAI panel added the sharpest version of the upside case. Alexander predicted a step change in what one person can do: “We are now going to enter a situation where the most productive individuals could be running unicorn companies, and folks who are less productive are, well, where they are today.” His prescription: hire high-agency people and structure teams to unlock their creativity, then scale it. Alexander framed it as an old pattern, not a new one; since the history of our species, someone who is good at using tools is faster and more productive than someone who is not.

The steal: pick a specific workflow the model companies will never prioritize (your 10%+) and build the context and product around it. Then staff for leverage, hire high-agency people who can each drive AI across multiple functions, because the ceiling on individual output just jumped.

7. The OpenAI panel: a 3-layer playbook for deploying agents safely

The panel with OpenAI’s Alexander produced the cleanest framework in all three sources for deploying agents without spooking the security team. It starts with a simple rule: an agent acting for you inherits exactly your permissions, no more. “If they’re running on behalf of me, these agents should not be given access to enterprise data and information that I did not have rights to see or use,” Arvind said, adding that they “should not have the ability to make any changes into the system that I could not make.” Then you scope further, because an agent is built for a specific set of tasks and shouldn’t wield all the powers you personally hold.

The third case is the new one: independent agents with their own identity. “Take your customer support team. Maybe you have 10 people in the team today and now this agent is the 11th member of that team,” Arvind said. Once an agent has its own identity rather than impersonating a person, you need a distinct permissions and guardrails model so it behaves like a good teammate, “adding positive value and not actually escalating and, you know, fighting with the humans inside.”

Alexander distilled the whole thing into three layers. “One is the control of what the agent can do. The second is the observability of that. And then lastly, starting to shift away from agents always impersonating us as people and having more agents that have a specific role with exactly the right permission set for that workflow, and that’s an independent agent.” His rollout advice was to start narrow and expand: connect agents to the most important context first, then add more incrementally. “Your security team will be very happy if you add one and then you add the other one. You can actually move really fast if you do it incrementally.”

This connects straight back to the ROI question from our interview: the enterprises that actually reach production-quality agents are the ones whose security teams never got a reason to say no. Glean’s three-layer model is how it sells “safe to deploy” alongside “cheaper to run.”

The steal: deploy agents in exactly this order. Mirror the user’s permissions, scope down to the task, then graduate the most valuable workflows to independent agents with their own identity, observability, and guardrails. Give your security team an incremental rollout they can approve one connection at a time, and you’ll ship faster than a big-bang launch that gets blocked.

The full interview Q&A

Every question we asked Arvind in the 20-minute interview, one by one, with his answers (lightly cleaned of filler words; the sections above expand on each theme).

Q1. How did you end up here? What’s your personal story as a founder?

Glean started in early 2019 while he was still co-founder at Rubrik, where fast growth had quietly wrecked internal productivity: “Everybody would complain about not being able to find things inside the company or connect with the right people to get help.” Before Rubrik he spent over a decade at Google building search systems, so the fix was obvious to him: “We should actually build a really good search product for all of our enterprise data and knowledge. And that’s how Glean got started.” Personally: grew up in India, moved to the States in the ‘90s for grad school, and has been in tech — and based in San Francisco — ever since.

Q2. You just raised your first round — who do you hire first?

“Hire software engineers, people who are going to build the product, because right now that is all we need.” Everything else — office, devices, food, finding early customers — he handled himself: “The first 10 people that we hired at Glean, they were all engineers. Maybe even the first 20, I don’t remember hiring anybody outside of R&D,” until the product reached a certain level of maturity.

Q3. How do you communicate the vision of the product to so many engineers?

“Simple ideas are easy to communicate. So if you think about Glean’s idea, it was actually very, very straightforward. It’s a Google for you in your work life. It’s a place where you go and ask questions when you’re looking for information or answers and we’ll bring the right information back to you.” The second half is belief: “They’ve got to feel that it’s going to be something big.”

Q4. Bootstrapping versus raising money — when should a founder raise?

“I personally have chosen the path of raising capital quickly... I want to focus all of my attention and energy into building the product. I don’t want to continuously think every month about, okay, well, how much can we invest? Can we really hire one more engineer or not? I like to go big and go big fast. So if you can, then I would recommend that, just raise capital. What’s the downside?” And on the classic objection: “Sometimes people feel that, oh, is it too early to dilute the company? And I don’t really believe in that.”

Q5. Was raising money easier the first time or with Glean?

Definitely easier the second time: “Investors are actually never looking to invest in great ideas. They’re actually looking to invest in people.” Ideas get transformed once you talk to customers, so investors underwrite persistence — “do you have the persistence to stay on that journey?” A track record answers that. He also notes today’s market is unusually founder-friendly: “There’s a lot of capital that is actually waiting to be deployed on those ideas.”

Q6. Dario Amodei says 50% of white-collar jobs could be erased in 5 years. What do you think?

“It’s a very bold statement and obviously I don’t think there’s any kind of deep analysis or research that was done to come up with the number 50. The fact that the number is 50 means it’s more figurative... But I kind of disagree with that regardless. I don’t think people are going to be replaced with AI in the sense that there are no jobs for us anymore. I think we’ll all have jobs and I think the nature of our jobs is going to change” — the same way his own work has continuously evolved in tech.

Q7. Uber reportedly burned its 2026 AI budget in four months without ROI — do enterprises actually see ROI from AI?

“It’s a wide spectrum, but businesses are definitely getting some value from AI today. If they’re getting none, they would have stopped by now.” The clearest value is basic knowledge-seeking — “every employee in the company becoming more productive, more knowledgeable” — “but it’s hard to measure. The fact that people can work faster oftentimes doesn’t translate into your P&L statement.” Larger companies with intentional programs, good metrics, and systematic process-by-process adoption are seeing results — “it is hard, it takes a lot of time, and AI vendors kind of made it seem like you’ll automatically and instantly get all these value savings.” Glean’s model is built for that reality: immediate productivity savings up front, then “many, many months understanding their business and helping them build those agents and get them to production level of quality.”

Q8. Have you found any use case you didn’t think of when you started?

“Many. Actually, almost all use cases are things that we didn’t imagine people would actually use our product for.” His list: a performance-management agent that “writes the first drafts” of annual reviews, customers “responding to RFPs” with AI, teams “reviewing legal contracts with AI,” and salespeople using AI “to figure out what messages to send to which one of their accounts.” Why it works: “Since Glean is so horizontal and it connects to all enterprise systems, it sort of functions as your chief of staff.”

Q9. Who do you reach out to to sell Glean in the organization?

“Because we’re horizontal, typically we’ll start with the CIO. The CIO typically thinks about how to bring company-wide AI technologies. And sometimes we do work with departmental leaders too, because for example, for salespeople, for CROs, we do a lot — there’s a lot in Glean that can help you truly improve the productivity of your sales team.”

Q10. OpenRouter is reportedly being acquired by Stripe — what do you think of that?

“I’m very familiar with them. They were one of the first model routing companies, but more sort of model access company than routing. And Glean does some of the same things that OpenRouter did, but we do it mostly in the enterprise context for large enterprises. But our business is actually very different. Model access is actually only one part of our overall solution. Largely, we think of Glean as the context and intelligence company” — the goal being to bring “the collective human intelligence and the know-how, the knowledge and experience of your enterprise in one place and then make it available to AI systems.”

Q11. For your agent builder — do customers build the agents themselves, or do you send in your engineers?

Customers build them, with Glean enabling rather than staffing: “We also have training and enablement programs. And in fact, sometimes we actually bring our partners to help build agents for our customers.”

Q12. What’s the main location of your customers — your go-to-market, roughly?

“We have customers worldwide. About 70% of the business is probably US and then 30% is the rest of the world right now. So we are in Europe, we are in Asia, in Canada, Latin America. And typically we work with mid-sized to large enterprises.”

Q13. To close — anything you’d like to share with founders starting a company right now?

He hears the same fear constantly: founders feel “every idea they come up with, you can just use an AI model or the model companies just come and do that... Is there any value to ideas left? What I fear and what I worry is, will people start to give up too soon and not become entrepreneurs?” His answer: “AI changes, if anything, actually creates more opportunity for entrepreneurs. Because it brings change, and whenever there is change, that is great for entrepreneurs. And model companies won’t be able to meet even 10% of all the needs that the market is going to have.”

What to steal

1. Recruit and fund around a one-sentence mission. If you can’t explain your product in the length of “Google for your work life,” it’s too complex to hire engineers around. Test yours on a non-technical friend this week; if they can repeat it, it’s tight enough.

2. Raise to eliminate distraction, not to optimize valuation. Arvind’s rule is to raise enough that you never spend a month calculating whether you can afford one more engineer. Frame your pitch around your persistence, because investors bet on people, not ideas.

3. Sell (and expect) months, not minutes. The “botsitting” tax is 6.4 hours a week per worker (Glean’s Work AI Institute) because vendors oversold instant magic. Commit to one high-cost process, build production-quality agents over months, and instrument the before/after so value actually reaches the P&L.

4. Sell horizontal platforms to the CIO, expand through surprise. Start with the company-wide AI owner, win one sharp department (CRO/sales) in parallel, then let the use cases customers invent — performance reviews, RFPs, contract review — pull your expansion and write your roadmap.

5. Own context, not the model, and route by task difficulty. Structured data is 5-10% of what a company knows; the moat is the years-long graph of how work actually happens, surfaced from emails, messages, and meeting notes. Then match model cost to task difficulty: the 36x spread for a 22% quality gain is pure waste, and Glean’s benchmark shows routing plus context can cut token costs 81% while winning 78% of comparisons.

6. Deploy agents in three layers so security says yes. Mirror the user’s permissions, scope to the task, then graduate the best workflows to independent agents with their own identity and observability. Incremental rollouts ship faster than big-bang launches that get blocked — and they free high-agency people to run what used to take whole teams.

Hope this was valuable!

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Onwards!

We went through the full 109-minute conversation with David Senra and pulled out every mechanic of how Kalanick ran his raises, from anchoring low to building a live demand curve, so you can run the same process on your next round.

In this guide, you’ll get:

1. How Kalanick Turned a $6B Offer Into $17.5B

2. The Fundraising Process That Created the $11.5B Upside

3. QED vs. Super Cycle: The 2 Pitch Modes

4. The 5-Room Auction That Drove Uber’s Valuation Up

5. How to Build a VC Demand Curve

6. The Kalanick Fundraising Tracker

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Everything below comes from one source: Kalanick’s own account of his Uber raises, given to David Senra across a 109-minute sit-down recorded this month. These are his conclusions, in his voice, with the exact numbers he cited. Treat them as his playbook, not verified fact, and adapt the mechanics to your own round.

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Garry Tan and Anish Acharya (that I previously interviewed last year) sat down to break down “the turns that shaped him, why founders should trust direct experience over consensus, how AI is allowing small teams to do more with less, and why he’s deeply optimistic about AI-native companies and the future of SF:

Garry Tan says there are companies today going from zero to $15M ARR in about four months with just two or three people and a few hundred skill files, while everyone else is still arguing over whether AI will kill white-collar jobs.

We pulled out the exact first-principles rules, agent build loops, and timing calls he now uses to advise founders, so you get the playbook without watching the whole thing. This is the difference between LARPing the crowd and building the company that eats an entire market with two or three people.

In this guide you’ll find:

1. The $2-4 billion mistake

2. Map versus territory: the thinking error that costs you the right co-founder

3. Why the solo founder is now viable (and where a co-founder still wins)

4. Per-seat SaaS is dying: the pricing and multiple Tan says are already obsolete

5. Agency and taste are the new scarce resource: the drill to actually build them

6. Token-max like a founder: pay to live in 2028

7. A markdown file is an employee: the loop that gets you to $15M ARR with two people

8. Clairvoyance over your whole company: give the agent visibility humans can’t hold

9. Every startup MUST make the mid-level org layer agents

10. The bottleneck is bureaucracy, not model intelligence: why that is the white pill

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1. The $2-4 billion mistake: chase what you uniquely know, not what is hot

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NVIDIA is worth over $5 trillion, and Jensen Huang opened his YC talk at Startup School by admitting the technology he founded the company on was “absolutely wrong,” that he didn’t even know the right way to do it, and that he learned 3D graphics from three textbooks he bought at Fry’s.

We went through the his masterclass and pulled out the actual operating system behind that run: the fundraising move that saved the company, the worldview test he uses to judge companies, the one skill he says beats coding, and the resilience discipline he’d give any founder starting today. You don’t have to watch it.

In this guide you’ll find:

1. Why NVIDIA’s founding bet was ‘absolutely wrong’ and confronting reality beat being right

2. The worldview test that makes a company fundable, not the chip

3. The radical-honesty move that turned a failed $12M contract into $5M of survival

4. Why systems thinking, not coding, is the skill to build now

5. The AI jobs narrative is backwards, and the numbers prove it

6. The ‘how hard can it be’ resilience loop Jensen says beats every skill

👋 Just a quick note before you continue!

Powerful models are only as useful as the company context they can access!

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💭 Remember!

As a premium subscriber you’ll also get access to all the other Claude resources we put together, such as:

• 1️⃣ The Claude Code Founder Guide

• 2️⃣ want to keep your job? read this.

• 3️⃣ claude just made outbound fully automatable.

• 4️⃣ 🚨 The Ultimate AI Fundraising Copilot (Built With Claude)

• 5️⃣ look what anthropic just released

• 6️⃣ The Ultimate Guide to Building Skills for Claude 🔥

• 7️⃣ The Prompting Skills Guide

• 8️⃣ The Garry Tan (YC) Playbook for Building with Claude

• 9️⃣ Claude. md Full guide

• 🔟 Understanding Claude Dispatch (Complete Guide)

• 1️⃣1️⃣ Claude For Finance: The CompIete Guide

• 1️⃣2️⃣ How I Rebuilt My SEO Strategy Inside Claude (The Playbook)

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Judging by the comments, it almost certainly did not happen or work as described. A coffee shop has close to zero need for a private LLM, and “every client after the first is close to pure margin” is the kind of line that sounds great but survives no contact with reality.

The underlying model, though, is real. Regulated small businesses genuinely cannot send client data to the cloud, and someone who can install a working offline document assistant for them has a product people will pay for.

So I researched and broke down the version that actually works: the one vertical that will pay, the exact hardware and software stack, real pricing and margins, the sales motion, and the limitations nobody in the replies mentioned.

In this guide you’ll find:

1. Why the coffee shop pitch fails and which SMBs actually pay for offline AI

2. The offline document Q&A stack: exact hardware and Ollama-based software to deploy

3. Realistic pricing and margins: what to charge vs the viral $2,100 flat fee

4. The sales motion: how to walk into a clinic or law office and close the install

5. The honest limitations that will burn you (and how to price around them)

💭 Remember!

As a premium subscriber you’ll also get access to all the other Claude resources we put together, such as:

• 1️⃣ The Claude Code Founder Guide

• 2️⃣ want to keep your job? read this.

• 3️⃣ claude just made outbound fully automatable.

• 4️⃣ 🚨 The Ultimate AI Fundraising Copilot (Built With Claude)

• 5️⃣ look what anthropic just released

• 6️⃣ The Ultimate Guide to Building Skills for Claude 🔥

• 7️⃣ The Prompting Skills Guide

• 8️⃣ The Garry Tan (YC) Playbook for Building with Claude

• 9️⃣ Claude. md Full guide

• 🔟 Understanding Claude Dispatch (Complete Guide)

• 1️⃣1️⃣ Claude For Finance: The CompIete Guide

• 1️⃣2️⃣ How I Rebuilt My SEO Strategy Inside Claude (The Playbook)

We’re already 70,000+ founders and operators inside!

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• Stay production‑ready: reviewable changes, collaboration, and safeguards that help teams ship without handoffs.

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1. Why the coffee shop pitch fails and which SMBs actually pay for offline AI

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What took each of those eight YC companies three months of nonstop work to build in 2005 can now be done by a coding agent in seven minutes, and most founders are reading that as their death sentence.

Sam Altman, who was a Stanford sophomore in that first batch building a location-sharing company called Loopt, closed Startup School 2026 by arguing the exact opposite: that this is the greatest moment in history to start a company.

We pulled a recent exclusive conversation of Sam with Garry Tan apart into the specific moves Altman says convert collapsing build costs into trillion-dollar outcomes, so you do not have to watch it.

In this guide you’ll find:

1. Why a 7-minute agent task means you should build a MORE ambitious company, not a smaller one

2. Why hard tech jumped from 5% to 25% of YC, and the timing signal that says build now

3. The conviction test: only 50 people believed AGI was possible, and 45 worked at OpenAI

4. How Altman met his OpenAI co-founder 8 years early, and the network strategy behind it

5. Why founding a startup is how you fight AI power concentration (and the underclass meme is wrong)

6. The demand numbers that prove the runway: inference growing 10x a year and a permanent compute shortage

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Framer is the pro website builder for creators, teams, and businesses that care enough to get every detail right. Now with Agents that work alongside you across the full website workflow: designing new pages, managing your CMS, editing content, and adding SEO all without leaving the tool where the real site lives. Trusted by teams at companies like Miro and Perplexity.

Agents bring speed and scale; you bring taste, judgment, and control. Together you can:

• Build and iterate faster: generate sections, refine layout and styling, and ship updates in minutes.

• Scale content with confidence: update CMS collections, localize copy, and keep metadata consistent across pages.

• Stay production‑ready: reviewable changes, collaboration, and safeguards that help teams ship without handoffs.

Launch Your Site With Framer Today!

Let’s begin!

1. Why a 7-minute agent task means you should build a MORE ambitious company, not a smaller one

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Two kids decided to start a financial services company on a walk home from sushi, in a sector so new the word “fintech” didn’t exist yet, and it took them two years to launch. That company is Stripe, and at Startup School 2026 Patrick Collison broke down the exact build sequence, why the lean startup playbook he’d tell his younger self to skip is now obsolete, and what Stripe’s live data says about the best time to start. We watched all 31 minutes of his conversation with YC’s Harj Taggar and pulled the stealable plays so you don’t have to.

The headline number: new businesses starting on Stripe are up around 2x year over year, the largest relative jump the company has ever recorded, larger even than the COVID surge. Collison’s read of his own formation data is blunt: by every objective metric he can see, it has never been a better time to start a company.

In this guide you’ll find:

1. Why Collison still learns hard things instead of outsourcing his brain to AI

2. The dropout math: why the cost is de minimis and nobody ever cared

3. Why Stripe worked: a concrete customer problem in a sector that didn’t exist yet

4. How Stripe justified a two-year wait to launch with just-in-time building

5. Why the lean startup is now the wrong playbook and what to do instead

6. What Stripe’s data says: new businesses up 2x and enterprises buying on day zero

👋 Just a quick note before you continue!

Building great AI systems and agents is quickly becoming one of the most important advantages a startup can have. So we’ve been building non-stop and this will become a much bigger part of PMF going forward.

From now on, as a premium subscriber, you’ll get access not just to the best resources on fundraising and growth (160+ Playbooks, 65+ Success Stories, 30+ Pitch Deck Collections, 25+ Investor Resources), but also:

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Alexandr Wang spent Scale AI’s first years walking into pitch meetings with great numbers and real revenue and getting rejected anyway.

Not one of the VCs across the table had ever trained a model. Data, in his words, was still “very unsexy.” A decade later, the same investors who passed on him write think pieces about how data is the biggest opportunity in AI.

Now Wang runs Meta’s Superintelligence Labs.

Before we get into his full conversation with Garry Tan at Y Combinator’s Startup School 2026, here is Scale AI’s funding journey:

• 2017, Series A: $4.5M raised at an undisclosed valuation, led by Accel.

• 2018, Series B: $18M raised at an undisclosed valuation, led by Index Ventures, with Accel and Y Combinator participating.

• 2019, Series C: $100M raised at a valuation of more than $1B, led by Founders Fund, with Accel, Coatue, Index Ventures, Spark Capital, and Thrive Capital.

• 2020, Series D: $155M raised at a valuation of more than $3.5B, led by Tiger Global.

• 2021, Series E: $325M raised at a $7.3B valuation, co-led by Dragoneer, Greenoaks Capital, and Tiger Global.

• 2024, Series F: $1B raised at a $13.8B valuation, led by Accel, with Amazon, Meta, Nvidia, and a long list of strategic and institutional investors.

• 2025, Meta deal: Meta invested $14.3B for a 49% stake, valuing Scale AI at more than $29B. Scale remained independent, while Wang joined Meta to lead its superintelligence push.

We went through his full conversation with Garry Tan and pulled out the parts worth stealing: the first-principles test that told him data was the wedge before anyone else saw it, the conviction playbook that explains why the smartest investors in the room were wrong for years, the exact agentic-loop mechanics that now beat a team of 100 engineers.

Bonus: at the end of the newsletter, you will also find Scale AI’s Series C pitch deck!

Inside you will find:

1. How Scale Really Started

2. Conviction Before Consensus

3. David vs Goliath Is Over: How AI Agents Turn Your Startup Into a Mecha-Goliath

4. How Wang Rebuilt a Frontier Lab in 9 Months

5. Vision Beats Intelligence: Why Ambition Is the Scarce Resource Now

6. The Agentic Loop That Beat 100 Engineers

7. What to Steal From Wang

8. Bonus: Scale AI Series C Pitch Deck

   

Launch your site faster than ever with Framer - now with Agents that make every part of your workflow effortless

Framer is the pro website builder for creators, teams, and businesses that care enough to get every detail right. Now with Agents that work alongside you across the full website workflow: designing new pages, managing your CMS, editing content, and adding SEO all without leaving the tool where the real site lives. Trusted by teams at companies like Miro and Perplexity.

Agents bring speed and scale; you bring taste, judgment, and control. Together you can:

• Build and iterate faster: generate sections, refine layout and styling, and ship updates in minutes.

• Scale content with confidence: update CMS collections, localize copy, and keep metadata consistent across pages.

• Stay production‑ready: reviewable changes, collaboration, and safeguards that help teams ship without handoffs.

Launch Your Site With Framer Today!

This issue + Scale AI Pitch Deck is available exclusively to paid subscribers!

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Last weekend, Y Combinator packed the Chase Center and handed the floor to the people actually building the future of AI.

Over 30,000 founders applied, from 120+ countries. Acceptance rate: under 1%. And the ones who got in? Jensen Huang told them flat out: “If you’re here, you’re gonna make it.”

Two days. Ten stages. Jensen Huang, Sam Altman, Boris Cherny, Jeff Dean, Waymo’s Dmitri Dolgov, and more. each one dropping the kind of insight most people pay years of tuition (or failed startups) to learn.

I sat through all of it, read the notes, the transcripts, and every founder’s live thread, so you don’t have to. Inside you’ll find the key takeaways from these speakers:

• Garry Tan (President, YC)

• Jensen Huang (Founder and CEO, NVIDIA)

• Sam Altman (Co-founder and CEO, OpenAI)

• Boris Cherny (Head of Claude Code, Anthropic)

• Jeff Dean (Chief Scientist, Google DeepMind)

• Dmitri Dolgov (Co-founder and CEO, Waymo)

• Alexandr Wang (Chief AI Officer at Meta)

• Patrick Collison (Co-founder and CEO, Stripe)

• Chelsea Finn (Co-founder, Physical Intelligence)

• Peter Steipete (OpenClaw)

Let’s get into it!

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Here’s a good place to start:

🤖 AI Agent Templates
↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks
↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library
↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools
↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

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The strange part is that CEO Amjad Masad does not treat that number as a goal at all.

He warns that most AI companies posting growth like this are quietly dying underneath it, with churn approaching 100%, and that a headline like his own can hide a business that is falling apart.

So we went through hours of Masad’s interviews to find out what actually drove the run.

What follows is the sequence of moves behind the number: the layoff that turned into an all-in bet, the customer nobody else was chasing, the boring infrastructure that became the real moat and the honest way to read AI growth, including your own.

Inside you will find:

1. The Layoff That Became an All-In Bet on Agent

2. Build the Product That Gets Better When You Swap the Model

3. Chase the Billion Non-Engineers, Not the Developers

4. The Real Moat Is Two Years of Boring Infrastructure

5. Story Is a Growth Channel, Not a PR Function

6. How to Read AI Growth Honestly

7. What to Steal: The 6-Move Replit Playbook

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Here’s a good place to start:

🤖 AI Agent Templates
↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks
↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library
↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools
↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

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Instead of watching 2 hours of Netflix tonight, study this 40-minute masterclass from the founder of a $20B China AI company.

It's the clearest explanation I've seen of how Agent Swarms and AI systems actually work at scale.

I broke it down into the most actionable insights so you can apply them directly.

Here’s what you can learn today:

1. Why Kimi scales three dimensions at once instead of just adding tokens

2. Why token efficiency raises the ceiling on intelligence, not just the cost

3. Why long context is the capability that makes agents runnable for weeks

4. The 3 reward functions that stop agent swarms from collapsing or cheating

5. How Kimi fused all three dimensions into K2.5 (and why vision made text smarter)

6. The 24% token-efficiency jump hiding in Kimi’s next architecture

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Here’s a good place to start:

🤖 AI Agent Templates

↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks

↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library

↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools

↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

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The company reports going from $0 to over $100M in annualized revenue in roughly 9 months while selling an integrated RL post-training stack. They then raised a $130M Series A led by Radical Ventures with NVIDIA, Intel Capital, and Dell Capital.

We broke down the exact wedge, packaging, and customer-proof sequence behind that run so you can steal the playbook, not just admire the number.

In this guide you’ll find:

1. What Prime Intellect actually sells: the Open Superintelligence Stack, layer by layer

2. The open-source wedge: how the Environments Hub reached 1,000+ environments before Lab launched

3. The positioning bet that unlocked demand: how ‘RL breaks open the frontier’ won teams away from closed-lab APIs

4. The 4 growth levers that drove 6,000+ customers and $100M ARR in under a year

5. The named-customer proof machine (Ramp, Arcee, Zapier) and how it replaced a sales team

6. The $130M Series A: who backed it, at what traction, and the angel list

7. 5 lessons to steal from Prime Intellect’s 9-month run

👋 Just a quick note before you continue!

From now on, as a premium subscriber, you’ll get access not just to the best resources on fundraising and growth (160+ Playbooks, 65+ Success Stories, 30+ Pitch Deck Collections, 25+ Investor Resources), but also:

Annual paid subscribers now unlock exclusive deals on the AI tools founders actually use every day.

Here’s what’s live on the perks page right now:

• Framer: 1 year free

• MITO: 60% off for 6 months

• Gojiberry: 50% off for 6 months

• Cubic: 50% off your first month

• Omnia: 20% off

• Granola: 1 month free

• Slashy: 1 month free

• QuiverAI: 1 month free

• Throxy: 50% off for 3 months

These deals are worth more than $3,000.

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Elon Musk does not outperform entire industries because he works longer hours, hires more people or has access to better technology.

He operates his companies using a distinct system for questioning assumptions, removing complexity, accelerating learning, identifying exceptional talent and concentrating resources on the biggest constraint.

To understand how that system actually works, I studied three of the clearest breakdowns of Musk’s operating methods:

David Senra’s 110-minute How Elon Thinks conversation with Eric Jorgenson, who spent five years and thousands of hours studying Musk to write The Book of Elon.

Relentless’s 102-minute conversation with Sequoia partner Shaun Maguire, Why Elon Outcompetes Everyone, which explores Musk’s talent selection, capital allocation, risk tolerance and ability to compound knowledge faster than his competitors.

And Andrej Karpathy’s talk at Sequoia AI Ascent, where the former Tesla AI leader explained what it was actually like to operate inside one of Musk’s companies—and how small, highly technical teams, extreme ownership and direct access to the underlying work shaped Tesla’s execution.

Together, they reveal something more useful than another collection of Elon stories.

They reveal the Elon Operating System: the repeatable principles Musk uses to build companies that move faster, learn faster and achieve outcomes that initially appear physically or economically impossible.

In this guide, you’ll learn:

1. The five-step build algorithm, in the exact order Musk runs it

2. How Musk engineers his companies to fail on purpose

3. How Musk identifies talent and creates extreme accountability

4. Why Musk keeps teams small, technical and close to the work

5. The mission-and-pain layer beneath the operating system

6. How Musk allocates capital and attacks constraints

7. The portable Elon OS checklist

👋 Just a quick note before you continue!

From now on, as a premium subscriber, you’ll get access not just to the best resources on fundraising and growth (160+ Playbooks, 65+ Success Stories, 30+ Pitch Deck Collections, 25+ Investor Resources), but also:

Annual paid subscribers now unlock exclusive deals on the AI tools founders actually use every day.

Here’s what’s live on the perks page right now:

• Framer: 1 year free

• MITO: 60% off for 6 months

• Gojiberry: 50% off for 6 months

• Cubic: 50% off your first month

• Omnia: 20% off

• Granola: 1 month free

• Slashy: 1 month free

• QuiverAI: 1 month free

• Throxy: 50% off for 3 months

These deals are worth more than $3,000.

Get 30% off forever

Here’s a good place to start:

🤖 AI Agent Templates

↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks

↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library

↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools

↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

The rest of the article is only available to premium subscribers.

1. The 5-step build algorithm, in the exact order Musk runs it (and why running it backwards nearly killed Tesla and Starlink)

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This morning Sifted reported that Lovable is in talks to raise $300 million at a $13.2 billion post-money valuation, with Menlo Ventures set to lead the round.

If it closes at those terms, the company will have doubled its price tag in roughly six months!

Sit with the timeline for a second, because the pace is the whole story:

• Oct 2024 → raises $7.5M (Hummingbird Ventures, byFounders)

• Feb 2025 → raises $15M (Creandum, Visionaries Club)

• Jul 2025 → becomes a unicorn, $200M Series A at $1.8B (Accel, 20VC, Creandum, Hummingbird, byFounders, Visionaries Club, Evantic)

• Dec 2025 → raises $330M at $6.6B (Google, Menlo Ventures, NVIDIA, Salesforce Ventures, Databricks Ventures, Atlassian Ventures, HubSpot Ventures, T.Capital, Khosla Ventures, DST Global, EQT Growth, Kinship Ventures, Accel, Creandum, Evantic)

• Jul 2026 → reported talks at $13.2B

That is five financings in about twenty months, each at a higher multiple than the last, with a founder who at one point simply stopped answering the term sheets landing in his inbox at a $4 billion valuation because he didn’t feel like raising yet.

Most founders experience fundraising as a thing that happens to them. Lovable built a machine that made investors show up before it asked, compete once they were there and accept terms that got worse for them with every passing quarter.

Inside you will find:

🟧 The demand machine that took Lovable from a $7.5M pre-seed to a reported $13.2B in twenty months

🟧 The full details of all five rounds and why each fund and angel was chosen

🟧 The angel-first move that buys competence and distribution, not just a check

🟧 The public ARR cadence that made investors chase before Lovable ever asked

🟧 What Osika did when unsolicited $4 billion offers hit his inbox

🟧 How the product closed a $200M round before the pitch meeting even started

🟧 The investor-sequencing map for who to raise from at each stage

🟧 The six channels that put your growth in front of the people who write the checks

The next era of GTM runs on Attio.

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• Score every lead for fit and priority

• Catch renewals before they slip

• Get answers about your business in seconds

Loved by high-growth startups like Granola, Modal, and Railway, Attio is the agentic CRM for winning teams.

Try Now (For Free)

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks and fresh high-signal content every week.

Here’s a good place to start for fundraising:

🎯 The Deck Library
↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors
↳ How ElevenLabs Grew From $0 to $200M ARR in 2 Years + Their Pre-Seed Deck
↳ 70 Pitch Decks That Raised Over $1 Billion in 2024
↳ Anthropic Pitch Deck: This 10 Slides Predicted a $350,000,000,000 Company

💰 Investor Database & Tools
↳ The Family Office List
↳ How to Get Into YC: The Full F25 Application Pack
↳ The 10 Excel Templates Every SaaS & AI Founder Needs to Build, Survive, and Scale
↳ Creandum fundraising Toolbox, from Seed to Series A

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Here is what they say works:

• in-house creative, meaning video and editorial made by people who did this for a living

• owned channels, meaning an X account, a newsletter and a podcast

• A full-service team that runs launches end to end

The message to a founder is not “look what a16z offers” but rather “this is where you should be insisting too”.

The obvious objection is that you cannot borrow the team. No video crew, no editorial desk, no launch unit, no million-follower account. So it is easy to file New Media under “works if you already have a billion-dollar fund” and move on. That is the mistake.

The same motions are now things a two-person company can run on a normal week, at a smaller scale, if you know what to build and in what order. This guide is that order: what a16z is telling you works, why it works and the practical version of each motion you can actually run, with the tools that make it possible.

Inside you will find:

1. What a16z Means by New Media and the Channels They Bet On

2. How to Run an In-House Video Team of One

3. How to Write the Essays That Earn You Signal

4. How to Build Owned Channels That Guarantee You Distribution

5. How to Run a Launch That Drives Pipeline

6. How to Turn Your Network Into a Hiring and Signal Machine

7. How to Run the Whole Engine on a Founder’s Schedule

👋 Just a quick note before you continue!

Building great AI systems and agents is quickly becoming one of the most important advantages a startup can have. So we’ve been building non-stop and this will become a much bigger part of PMF going forward.

From now on, as a premium subscriber, you’ll get access not just to the best resources on fundraising and growth (160+ Playbooks, 65+ Success Stories, 30+ Pitch Deck Collections, 25+ Investor Resources), but also:

• 🤖 3+ New AI agent Templates Every Week

• 📚 A Growing Library of 20+ AI Agents

Get 7 day free trial

We’re already 70,000+ founders and operators inside!

The rest of the article is only available to premium subscribers.

Let’s begin!

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Lovable is not growing because it found one clever channel.

It is growing because it built a system where:

• the product creates delight

• delight creates sharing

• sharing creates trust

• trust creates growth

In Elena Verna’s framing, growth is a trust problem now.

When software becomes easier to build, functionality stops being enough. People can copy features. They can build alternatives. What they cannot copy as easily is:

• trust

• brand

• speed

• relevance

• emotion

• community energy

• product-led word of mouth

That is the real growth engine.

We broke down Lovable’s growth into a system that can be understood and, in parts, applied whether you’re early or already scaling.

1. Why growth is becoming a trust problem (not a channel problem)

2. How to use your product as the main growth channel

3. Why you need a “minimum lovable product,” not just a functional one

4. How to use founder-led social to drive early traction

5. How to turn employees into a distribution engine

6. Why AI-native companies blur roles (and why it matters)

7. Why avoiding the biggest mistake in early-stage growth (paid too early)

8. How to sequence organic and paid growth the right way

9. Why most communities fail (and how to build one that works)

10. How to turn users into your most effective growth channel (UGC)

11. How to define activation around real value, not vanity metrics

12. How to use continuous launches to drive retention and relevance

13. How to stay top-of-mind in fast-moving markets

14. How to design monetization for bursty AI usage

15. Why brand becomes critical as products commoditize

16. How to use brand and offline campaigns effectively (and when)

17. Why leveraging creators as a growth channel

18. How to turn free product moments into growth campaigns

19. Why speed acts as a trust signal

20. How to compete with incumbents using speed, culture, and distribution

Why it’s valuable

This guide connects a lot of scattered ideas into a more coherent model.

You can use this to:

• think about product as distribution, not just utility

• treat free usage as awareness, not just lost revenues

• prioritize organic pull before scaling paid

• design for sharing and visibility, not just usage

• avoid scaling a system that hasn’t earned trust yet

The full breakdown is available for paid subscribers. If you’re not one yet, you can join here:

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How is it even remotely normal that a 19-year-old scaled an app to ~$50M ARR in 18 months…

…then sold 100% of it to the category leader…

…with zero VC funding???

No seed. No Series A. All bootstrapped.

Just raw distribution and the most ruthless execution I’ve ever seen from any founder his age.

And now he just dropped an interview where he lays out exactly how they did it.

The actual mechanics: Attribution hacks, creative frameworks, campaign structures, UGC systems…

This is not an “AI startup” story.

This is a masterclass in modern consumer growth 🔥

Cal AI is a consumer app that lets you take a photo of your food and instantly get calories and macros.

Just:

📸 Snap → 📊 Calories + macros → Done.

That’s it.

In one of the most competitive consumer categories on earth (calorie tracking) this 19-year-old founder built this to:

• $30M revenue in 2025

• $5.7M revenue in January alone

• ~$50M ARR / run-rate

• Zero funding

• Then sold 100% of it to MyFitnessPal

Eighteen months. Here’s how:

What We’ll Break Down

1. Start With a Product That Demonstrates Itself in 2 Seconds

Why Cal AI built a product that demonstrates itself instantly on video.

2. Distribution Before Product

How they validated a content ecosystem before writing a single line of code.

3. The Brutal Early Days: 500 DMs → 2 Creators

How manual influencer outreach became their first growth engine.

4. The Economics That Made Influencer Marketing Profitable

The RPM vs CPM framework that made creator marketing profitable.

5. The Negotiation Tactics To Lower Creator Costs

How bundling, view guarantees, and DMs cut influencer costs dramatically.

6. The UGC Creative Strategy & How to Copy Them (INSANE🔥)

Why native content beats ads — and how Cal AI engineered viral creator videos.

7. Turning the Comment Section Into a Funnel

How pinned comments and fast replies turned curiosity into installs.

8. Building a Creator Discovery Machine

How they trained TikTok’s algorithm to constantly surface new influencers.

9. Scaling to 150 Creators

Why retainers created predictable distribution and lower acquisition costs.

10. The Paid Ads Multiplier: $2M/year to $50M/year

How influencer videos became the creative engine for profitable Meta and TikTok ads.

11. Using Apple Search Ads to Attack Competitors

Using Apple Search Ads to intercept competitor demand.

12. Understand & Optimize Conversion Engineering

How onboarding psychology and trial design increased subscription conversion.

Apply to The Pitch by Deel now

A teenager built a consumer subscription app to ~$50M run-rate in a year and a half, bootstrapped, in one of the most competitive categories on earth.

And then sold it to his biggest competitor. explain here what it is

Let’s break down exactly what he revealed, and why this might be one of the most instructive consumer growth case studies of the last few years 🔥

How Cal AI Went From a High School Project to ~$50M ARR 🔥

The Exact Growth Systems They Used (In Order)

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Everyone is arguing about how powerful AI is getting. Wrong conversation!

What matters now is where leverage survives once intelligence is cheap, and where it doesn’t.

This week, in OpenAI’s Town Hall, Sam Altman wasn’t making announcements, but he was outlining constraints. The kind that quietly determine which startups work and which ones never compound.

Below are 12 moments from that conversation that actually matter if you’re building: reordered, compressed, and interpreted for people who care about product, distribution, and durability.

First impressions matter.

With Framer, early-stage founders can launch a beautiful, production-ready site in hours. No dev team, no hassle.

Join hundreds of YC-backed startups who launched here and never looked back.

Pre-seed and seed-stage startups new to Framer can enjoy:

• One year free: Save $360 with a full year of Framer Pro, free for early-stage startups.

• No code, no delays: Launch a polished site in hours, not weeks, without hiring developers.

• Built to grow: Scale your site from MVP to full product with CMS, analytics, and AI localization.

• Join YC-backed founders: Hundreds of top startups are already building on Framer.

Apply to claim your free year →

What We’ll Break Down 🔥

1. Jevons Paradox for AI: Cheaper Intelligence = More Software

2. WhyGTM Is Still the Bottleneck

3. When the Platform Doesn’t Know the Interface
Why OpenAI’s uncertainty signals category creation, not stability.

4. The 100× Cost Curve Is the Real Disruption
How infrastructure deflation unlocks entire categories before features do.

5. From Tools to Solved Problems
Why continuously generated software changes the unit of value.

6. The Founder Trap: Betting on Future Model Upgrades
Why relying on model progress is not a moat.

7. The Agent Trust Layer: Why Verification > Autonomy

8. Integrations Are Not Defensibility
As models learn tools instantly, advantage shifts to workflows and proprietary data.

9. The Hidden Risk Curve of Agents
Permission creep, rare failures, and the rise of monitoring infrastructure.

10. Memory as the New Switching Cost
Why persistent context, not raw intelligence, compounds defensibility.

11. Org Design in an Abundance Era
Leverage per employee becomes the only metric that matters.

12. Assume 100× Everything
How to think like a builder in a world where intelligence is no longer scarce.

Let’s get into this 🔥

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Most founders don’t fail because the product sucks.

They fail because they keep acting like the best employee when the company needs a CEO.

At scale, “being productive” stops meaning what you can do today. It means what the company can do without you. If you don’t make that switch, you become the bottleneck. hen the board, the market, or burnout makes the decision for you.

This isn’t motivational. It’s statistical. In a study of 1,156 VC-backed U.S. IPOs, 40.8% changed CEOs between the first VC round and the IPO. And HBR notes founder-CEO transitions are 2–3x more likely to fail or cause a performance drop than transitions between nonfounder CEOs.

So this guide is about one thing: killing the “founder mode” habits that stall growth, and replacing them with CEO systems.

It includes:

1. The 4 CEO fundamentals founders learn too late

2. The step-by-step process to remove yourself as the bottleneck

PMF is read by more than +1000 different VC firms & tens of thousands of the fastest growing startup founders including multiple YC founders.

Want to put your company in front of them?

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1. The 4 CEO fundamentals founders learn too late

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